TradeSight- Simple Moving Average (SMA) Crossover Backtesting in Python

This project implements a straightforward backtesting framework for a simple moving average (SMA) crossover trading strategy using Python and Pandas.

Deployed with StreamLit- https://backtesting-ezi3yt44q5da2siz2eu4c6.streamlit.app/

It allows you to:

1. Fetch and clean historical stock data
2. Calculate fast and slow SMAs (e.g., 20-day and 50-day)
3. Generate buy/sell signals based on SMA crossovers
4. Simulate trading behavior with capital allocation
5. Track portfolio value over time
6. Visualize trades and portfolio performance

Strategy Logic

Buy Signal: When the short-term SMA crosses above the long-term SMA Sell Signal: When the short-term SMA crosses below the long-term SMA

The backtester assumes 100% capital is invested on each buy and fully liquidated on each sell (no position sizing logic yet).

Outputs

1. Number of Buy and Sell signals

2. Final Portfolio Value

3. Line plot of: Closing prices, SMA lines, Buy/Sell signals, Portfolio value over time

Tools & Libraries Used

1. pandas – data manipulation

2. numpy – numerical computations

3. matplotlib – visualization

4. yfinance – fetching historical stock data